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Tumor Segmentation

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Papers

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Showing 451–460 of 786 papers

TitleStatusHype
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI—0
Automatic segmentation of kidney and liver tumors in CT images—0
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark—0
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method—0
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